As is the case for most important categories of technology, discussions of BI can get confused. I’ve remarked in the past that there are numerous kinds of BI, and that the very origin of the term “business intelligence” can’t even be pinned down to the nearest century. But the most fundamental confusion of all is that business intelligence technology really is two different things, which in simplest terms may be categorized as user interface (UI) and platform* technology. And so:

The UI aspect is why BI tends to be sold to business departments; the platform aspect is why it also makes sense to sell BI to IT shops attempting to establish enterprise standards.

The UI aspect is why it makes sense to sell and market BI much as one would applications; the platform aspect is why it makes sense to sell and market BI much as one would database technology.

The UI aspect is why vendors want to integrate BI with transaction-processing applications; the platform aspect is, I suppose, why they have so much trouble making the integration work.

The UI aspect is why BI is judged on … well, on snazzy UIs and demos. The platform aspect is a big reason why the snazziest UI doesn’t always win.

*I wanted to say “server” or “server-side” instead of “platform”, as I dislike the latter word. But it’s too inaccurate, for example in the case of the original Cognos PowerPlay, and also in various thin-client scenarios.

Key aspects of BI platform technology can include:

Query and data management. That’s the area I most commonly write about, for example in the cases of Platfora, QlikView, or Metamarkets. It goes back to the 1990s — notably the Business Objects semantic layer and Cognos PowerPlay MOLAP (MultiDimensional OnLine Analytic Processing) engine — and indeed before that to the report writers and fourth-generation languages of the 1970s. This overlaps somewhat with …

… data integration and metadata management. Business Objects, Qlik, and other BI vendors have bought data integration vendors. Arguably, there was a period when Information Builders’ main business was data connectivity and integration. And sometimes the main value proposition for a BI deal is “We need some way to get at all that data and bring it together.”

Security and access control – authentication, authorization, and all the additional As.

Scheduling and delivery. When 10s of 1000s of desktops are being served, these aren’t entirely trivial. Ditto when dealing with occasionally-connected mobile devices.

However, there will be exceptions, mainly on the machine-generated side. Where data creation and RAM data storage are getting cheaper at similar rates … well, the overall cost of RAM storage may not significantly decline.

Getting more specific than that is hard, however, because:

The possibilities for in-memory data storage are as numerous and varied as those for disk.

The individual technologies and products for in-memory storage are much less mature than those for disk.

Solid-state options such as flash just confuse things further.

Consider, for example, some of the in-memory data management ideas kicking around. Read more

This is one of a series of posts on business intelligence and related analytic technology subjects, keying off the 2011/2012 version of the Gartner Magic Quadrant for Business Intelligence Platforms. The four posts in the series cover:

The heart of Gartner Group’s 2011/2012 Magic Quadrant for Business Intelligence Platforms was the company comments. I shall expound upon some, roughly in declining order of Gartner’s “Completeness of Vision” scores, dubious though those rankings may be. Read more

I seem to be in the mode of sharing some of my frameworks for thinking about analytic technology. Here’s another one.

Ultimately, there are six useful things you can do with analytic technology:

You can make an immediate decision.

You can plan in support of future decisions.

You can research, investigate, and analyze in support of future decisions.

You can monitor what’s going on, to see when it necessary to decide, plan, or investigate.

You can communicate, to help other people and organizations do these same things.

You can provide support, in technology or data gathering, for one of the other functions.

Technology vendors often cite similar taxonomies, claiming to have all the categories (as they conceive them) nicely represented, in slickly integrated fashion. They exaggerate. Most of these categories are in rapid flux, and the rest should be. Analytic technology still has a long way to go.

Yes. The integration of predictive analytics with other analytic or operational technologies is still ahead of us, so there was a lot of value to be gained from SPSS beyond what it had standalone. (That said, I haven’t actually looked at the numbers, so I have no comment on the price.)

By the way, SPSS coined the phrase “predictive analytics”, with the rest of the industry then coming around to use it. As with all successful marketing phrases, it’s somewhat misleading, in that it’s not wholly focused on prediction.

2) how does it position IBM vs. competitors?

IBM’s ownership immediately makes SPSS a stronger competitor to SAS. Any advantage to the rest of IBM depends on the integration roadmap and execution.

3) How does this particularly affect SAP and SAS and Oracle, IBM’s closest competitors by revenue according to IDC’s figures?

If one of Oracle or SAP had bought SPSS, it would have given them a competitive advantage against the other, in the integration of predictive analytics with packaged operational apps. That’s a missed opportunity for each.

IBM’s done a good job of keeping its acquired products working well with Oracle and other competitive DBMS in the past, and SPSS will surely be no exception.

Obviously, if IBM does a good job of Cognos/SPSS integration, that’s bad for competitors, starting with Oracle and SAP/Business Objects. So far business intelligence/predictive analytics integration has been pretty minor, because nobody’s figured out how to do it right, but some day that will change. Hmm — I feel another “Future of … ” post coming on.

I chatted yesterday with the general business side (as opposed to the trading operation) of a household-name brokerage firm, one that’s in no immediate financial peril. It seems their #1 analytic-technology priority right now is changing planning from an annual to a monthly cycle.* That’s a smart idea. While it’s especially important in their business, larger enterprises of all kinds should consider following suit. Read more

A few days ago I tore into the Gartner Magic Quadrant for Data Warehouse DBMS. Well, the 2009 Gartner Magic Quadrant for Business Intelligence Platforms is out too. Unlike the data warehouse MQ, Gartner’s BI MQ clusters its “Leaders” together tightly. But while less bold, the Business Intelligence Magic Quadrant’s claims are just as questionable as those in data warehousing.